In a typical radio base station (RBS), a digitized and serial internal interface may establish a connection between radio equipment control (REC) and radio equipment (RE) of the RBS. Such an interface is specified in the specification for common public radio interface (CPRI), available from http://www.cpri.info/spec.html. For an RBS system supporting the universal mobile telecommunication standard, long term evolution/long term evolution advanced (UMTS LTE/LTE-A), a complex valued time domain OFDM signal is typically transported between remote radio unit (RRU) and baseband unit (BBU) of the RBS via the CPRI. The in-phase (I) and quadrature (Q) modulated data (i.e. digital baseband signals with I and Q components per complex valued sample) may be denoted I/Q data in user plane information of the CPRI.
As requirements on the maximum data rate between RRU and BBU of the RBS (and the data rate of backhauling between eNBs) increase (e.g. in OFDM due to increased radio frequency (RF) bandwidth, increased number of carriers per sector, multiple antenna technology (e.g. multiple input multiple output—MIMO), coordinated multi point (CoMP), cascading and multihop topologies of the RRU, etc.), increasingly higher requirements are posed on the CPRI implementation (e.g. number of CPRI ports, speed and cost of fiber module, and operating speed of serializer/deserializer units (SerDes)) for accelerating the CPRI line bit rate.
A typical CPRI implementation comprises transceiver modules, which are hardware units at both ends (RRU and BBU) of an optical link. As indicated above, the increasing BBU-RRU connectivity requirements pose challenges to speed and capacity of the optical transceiver modules. To meet these increasing requirements for aggregated data rate of the internal RBS interface, the CPRI line bit rate may be increased and/or the number of CPRI ports allocated for RRU and BBU connection may be increased. Another possibility is to compress the data to be transferred over the CPRI, for example by using fewer bits to represent each sample.
Therefore, there is a need for methods and arrangements that reduces the amount of data, in particular complex valued OFDM data, for transfer over the CPRI. Various techniques may reduce the burden on the CPRI interface, including time domain schemes (e.g. reducing sample rate, reducing sample bit length via truncation) and transformed domain schemes (e.g. sub-carrier compression in the frequency domain).
Data rate may be a limiting factor also in other technology areas including data transfer, such as, for example, satellite communication and remote sensing. In relation to such technology areas, some compression algorithms for I/Q baseband data and implementations thereof are known and can generally be divided into three types (scalar compression, vector compression and transformed domain compression). Examples of scalar compression may be found in “Block floating point for radar data” by E. Christensen, IEEE transactions on Aerospace and Electronic Systems, vol. 35, no. 1, January 1999, pp. 308-318 and in “Block Adaptive Quantization of Magellan SAR Data” by R. Kwok, W. Johnson, IEEE transactions on Geoscience and Remote Sensing, vol. 27, no. 4, July 1989, pp. 375-383.
Signal-to-quantization-and-saturation-noise ratio for both fixed point and floating point uniform quantization representation are analytically expressed in “Block floating point for radar data” by E. Christensen, IEEE transactions on Aerospace and Electronic Systems, vol. 35, no. 1, January 1999, pp. 308-318.
Block floating point quantization (BFPQ) may be considered as a special case of floating point representation or as a tradeoff between fixed point and floating point representation.
In a typical quantization approach, a block of L consecutive samples is assigned a shared scaling factor corresponding to the largest magnitude among the samples in the block, i.e.[x0, . . . ,xL-1]=[m0, . . . ,mL-1]2EXP;ml=xl2−EXP,EXP=1+S+log2└maxl=0, . . . ,L-1|xl|┘,where the range of each mantissa of the block is in the interval |ml|ε[0; 2−S] for l=0, 1, . . . , L−1, the integer S is a scaling factor used to prevent overflow, and └.┘ denotes the floor operation that rounds a scalar value down to its closest integer.
In BFPQ, each block of samples is separately quantized to block-floating-point representation, and the shared block exponent EXP is represented only once for all samples within each block. If the magnitudes of mantissas |ml| are represented with bm bits and the block exponent EXP is represented with bEXP bits, each scaled sample is represented with (1+bm)+bEXP/L bits on the average.
Due to signal power fluctuation, bm and bEXP may be different for each block. Hence, the average bit length per sample may vary from block to block, thus introducing fluctuation in the instantaneous resulting data rate, which in turn may cause difficulties in timing and frame synchronization. There may also be data rate fluctuation between the antenna paths in multi antenna applications, which may lead to adverse impact on time alignment between the antennas.
Many known techniques for reducing CPRI requirements suffer from one or more drawbacks such as, for example, high complexity, signaling overhead, real-time implementation difficulties, latency problems, information distortion, dynamic range limitations, and difficulties to control transfer data rate.
Therefore, there is a need for alternative solutions that reduces the amount of data, in particular complex valued OFDM data, for transfer over the CPRI. More particularly, there is a need for methods and arrangements that compress/de-compress complex valued OFDM data.